- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa
摘要： Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
关键词： multispectral image,Suaeda salsa,LiDAR data,fine classification,Aboveground biomass,hyperspectral image
更新于2025-09-23 15:23:52
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Fine Classification of Typical Farms in Southern China Based on Airborne Hyperspectral Remote Sensing Images
摘要： In the southern part of China, peculiar land fragmentation so that crop planting is characterized by small planting area of a single block, alternate cropping in multiple plots and diversified planting in space. Based on the unique crop planting characteristics in southern part of China, this paper take typical southern farm in Honghu City, Hubei Province as an example, adopting the platform of unmanned aerial vehicle imaging spectrometer to obtain the “double high” (high spectral and high spatial resolution) images at the same time. To complete the crop fine classification of 'double high' images , the CNN- CRF algorithm is proposed. The CNN-CRF algorithm acquires 91.5% accuracy with only 1% train samples on remote sensing images, which performs far better than most traditional classification approaches.
关键词： Conditional Random Fields (CRF),Convolutional Neural Network (CNN),Fine Classification,Airborne hyperspectral
更新于2025-09-10 09:29:36